Fast and Efficient Dataset Deduplication (FED)

• FED is a framework that detects and removes duplicate or near-duplicate items in large-scale datasets using advanced matching techniques including fuzzy and semantic methods.
• It leverages algorithmic paradigms such as MinHash, GPU acceleration, Bloom filters, and vectorized chunking to optimize deduplication throughput and precision.
• FED integrates privacy-preserving protocols and efficient clustering algorithms to ensure scalability, data utility, and secure operations across diverse applications.

Fast and Efficient Dataset Deduplication (FED) addresses the detection and removal of duplicate or near-duplicate items—files, documents, images, or records—in large-scale datasets. The goals are high deduplication accuracy (often including fuzzy or semantic matching), extreme computational and memory efficiency, and preservation of utility for downstream applications. Modern approaches to FED span chunking, hashing, streaming, learning-based, and privacy-preserving paradigms, commonly integrating vectorization, hardware accelerations, or advanced data structures to optimize throughput and scalability.

1. Core Algorithmic Paradigms

FED encompasses several computational paradigms optimized for different data modalities and deployment environments:

MinHash and LSH Pipelines: Widely adopted for text and entity deduplication, MinHash signatures are generated for sets of k-grams (shingles) extracted from documents. Locality-Sensitive Hashing (LSH) arranges these signatures into bands/r-rows per band, grouping items sharing a band into candidate sets for pairwise similarity assessment. For large N, LSH reduces $O(N^2)$ pair enumeration to $\sim O(N)$ bucketed candidate generations, with the S-curve $P_{LSH}(s) = 1-(1-s^r)^b$ controlling the detection threshold (Son et al., 2 Jan 2025, Shenoy et al., 2017).

GPU-Accelerated Deduplication: GPU-native pipelines such as FED (Son et al., 2 Jan 2025) optimize MinHash generation and LSH bucketing through per-shingle rolling hash kernels and tiled pairwise similarity comparisons (SIMD matrix-multiplies on banded signatures). Buckets are processed in parallel, and clustering is performed on the CPU with near-linear time union-find algorithms.

Bloom Filter-Based Streaming: In streaming or resource-constrained settings, variants such as Reservoir Sampling-based Bloom Filters (RSBF), Biased-Sampling Bloom Filters (BSBF), and Load-Balanced variants maintain small rolling fingerprints. Insert/delete operations and strategic bit resets ensure that both false positives and negatives are controlled and converge to zero as the stream progresses (Bera et al., 2012).

Hashless and Vectorized Chunking: For data backup and storage, content-defined chunking (CDC) divides input streams into variable-size chunks suitable for block-level deduplication. Lightweight, hashless chunking (e.g., SeqCDC) identifies strictly monotonic runs to place chunk boundaries, replacing expensive Rabin hash computations with vectorized (SSE/AVX) compare operations and content-defined region skipping (Udayashankar et al., 27 May 2025).

Perceptual and Semantic Hashing: On image and multi-modal data, FED often employs perceptual hashes—e.g., DCT-based pHash for images—to rapidly fingerprint visual content, or pre-trained semantic embeddings with cluster-and-prune strategies (e.g., SemDeDup, spherical k-means with thresholded cosine similarity) to identify semantic duplicates (Abbas et al., 2023, Adimoolam et al., 2023).

Blocking and Entity Resolution: Structurally diverse tabular data leverages learned hash/blocking schemes (e.g., CBLOCK’s BlkTree, QueryER's meta-blocking operators) to partition records with similar keys, facilitating scalable, schema-agnostic deduplication in integration or query contexts (Sarma et al., 2011, Alexiou et al., 2022).

Privacy-Preserving Deduplication: In cross-organizational or federated contexts, secure multi-party computation (e.g., secure PSI, fuzzy PSI via otFPSI) and soft deduplication via reweighting (FedRW) are utilized to perform deduplication without raw data exposure, balancing security, recall, and practical overhead (Rausch et al., 9 Apr 2026, Ye et al., 10 Nov 2025).

2. Mathematical Foundations and Computational Models

Several metrics and probabilistic models underpin FED systems:

• Jaccard Similarity: $$\displaystyle J(A, B) = \frac{|A \cap B|}{|A \cup B|}$$ is the canonical measure for set-based deduplication (text, k-grams, images).
• MinHash Property: $$\Pr[\text{MinHash}_h(A) = \text{MinHash}_h(B)] = J(A,B)$$, allowing similarity estimation by the fraction of shared signature coordinates.
• Bloom Filter False Positive/Negative Bounds: The per-insertion and deletion rules for various FED-Bloom Filter variants are designed to optimize FPR and FNR convergence (Bera et al., 2012).
• Deduplication Ratio and Utility: Deduplication is evaluated by reduction ratio $r=1-|D_{reduced}|/|D_{original}|$ and the impact of pruning on downstream utility (accuracy, perplexity, generalization).
• Scalability Complexity: Standard all-pairs complexity is $O(N^2)$, but practical schemes via LSH, streaming, and clustering techniques bring effective scaling down to nearly linear or subquadratic for web-scale datasets (Son et al., 2 Jan 2025, Bore et al., 2 Jun 2026).

3. System Architectures and Implementation Strategies

A robust FED pipeline typically comprises:

• Preprocessing: Tokenization/shingling for text, normalization for images (e.g., resizing, color space conversion), and sometimes initial chunking.
• Fingerprinting: MinHash or perceptual hash generation, often with hardware-vectorized code paths or low-memory streaming.
• Candidate Generation: LSH bucketing/banding, blocking keys, or online neighbor search (FOLD's HNSW approach) to narrow candidate pairs.
• Verification and Clustering: Exact similarity computation for candidates; union-find or triangle-inequality-based clustering to group duplicates (Shenoy et al., 2017).
• Postprocessing and Integration: Deduplicated output is commonly fed to downstream hashing (e.g., MD5, SHA-256), storage, or learning modules. Integration can include chunk-level dedup for backup systems (SeqCDC), privacy-aware sample reweighting (FedRW), or cross-node synchronization and auditing (FASTEN) (Udayashankar et al., 27 May 2025, Ye et al., 10 Nov 2025, Ahmed et al., 2023).

4. Comparative Analysis and Performance Benchmarks

FED methods are systematically benchmarked on throughput, scalability, accuracy, and deduplication quality:

Method	Scale/Setting	Throughput	Dedup. Quality	Memory/Compute	Reference
FED GPU (MinHash)	30M docs, 4xV100	366s	Jaccard ≥0.95 vs. baseline	58x CPU, 6.1x prior	(Son et al., 2 Jan 2025)
Bloom Filter	1B-item stream	>1GB/s	RLBSBF: FNR<1%, FPR ~0.1–2%	Linear, in-memory	(Bera et al., 2012)
SeqCDC	16KB, AVX-512	~30GB/s	Within 1–6% of best CDC	15x classic CDC	(Udayashankar et al., 27 May 2025)
SemDeDup	440M imgs (LAION)	6h cluster	Remove 50%, <1% perf. loss	O(n²/k) via clustering	(Abbas et al., 2023)
FOLD	30M docs	220–550/s	Recall 0.93–0.97 at all scales	Streaming, constant	(Bore et al., 2 Jun 2026)
LSHBloom	39M docs (peS2o)	5.2h	F1=0.90, FPR=1e-5, 0.6% space	2.7x speedup	(Khan et al., 2024)

Empirical findings include:

• GPU pipelines are critical at billion-scale.
• RLBSBF and other bias-driven Bloom variants substantially reduce FNR with minimal FPR penalty.
• Hashless vectorized chunking shifts the throughput-vs-ratio trade-off, yielding high ingestion with negligible quality loss.
• Semantic deduplication via embeddings achieves 2x+ dataset shrinkage with minimal impact on task accuracy.
• Privacy-preserving deduplication protocols (otFPSI, FedRW) offer orders-of-magnitude practical speedup over prior secure matching approaches while maintaining strong privacy and negligible false positive rates (Ye et al., 10 Nov 2025, Rausch et al., 9 Apr 2026).

5. Practical Integration, Trade-offs, and Limitations

Practical deployment of FED systems must consider:

• Parameter Tuning: LSH parameters (band count, rows/band), chunk size targets, Bloom filter sizes, and similarity thresholds require calibration on held-out or synthetic data for optimal balance of recall, precision, and speed (Shenoy et al., 2017, Udayashankar et al., 27 May 2025).
• Resource Constraints: Main memory, disk I/O bandwidth (for Cassandra/DB-backed LSH), and GPU availability directly impact candidate generation and clustering throughput (Shenoy et al., 2017, Son et al., 2 Jan 2025).
• Deduplication Quality vs. Throughput: Larger chunk sizes and filters favor speed and index size at the expense of possibly lower duplicate recall, especially for short or partial duplicates (Udayashankar et al., 27 May 2025, Khan et al., 2024). Methods such as RLBSBF, BSBFSD, or cluster-based union-find offer tunable accuracy/computation trade-offs (Bera et al., 2012, Shenoy et al., 2017).
• Semantic vs. Exact Deduplication: Embedding-based methods (SemDeDup) identify near-duplicates beyond surface similarity but depend on high-quality pre-trained encoders and cannot identify redundancies across modalities unless a joint representation is available (Abbas et al., 2023).
• Privacy and Security: Secure PSI and reweighting schemes (FedRW, xDup) add cryptographic communication/compute overhead but remove the need for trusted third parties and can deliver 10–100x speedup over earlier SMC protocols without privacy sacrifice (Rausch et al., 9 Apr 2026, Ye et al., 10 Nov 2025).
• Failure Modes: Limitations arise with data exhibiting substantial geometric/semantic variation, adversarial hash collisions, highly structured duplications, or severe under/overfit of threshold parameters (Adimoolam et al., 2023, Khan et al., 2024).

6. Emerging Directions and Research Challenges

Current and future research in FED includes:

• Online fuzzy deduplication: Streaming ingestion into incrementally updated vector indices (FOLD, HNSW-based methods) to maintain flat-throughput and recall at web scale without full rescans (Bore et al., 2 Jun 2026).
• Extreme-scale deduplication: Memory-efficient indices (LSHBloom) and hybrid structures for deduplicating at the multi-billion item scale while maintaining sub-linear index growth and sub-millisecond query costs (Khan et al., 2024).
• Federated and privacy-preserving deduplication: Clientside reweighting (rather than hard deletion) to mitigate privacy leakage in distributed learning; advanced PSI techniques for fuzzy matches in non-colluding multi-party scenarios (Ye et al., 10 Nov 2025, Rausch et al., 9 Apr 2026).
• Semantic generalization: Integration of neural embeddings with classic LSH for multi-modal, cross-domain, and multi-lingual deduplication while retaining strict precision at high recall (Abbas et al., 2023, Bore et al., 2 Jun 2026).
• Efficient deployment: Adaptive parameter tuning, integration with data curation and ingestion pipelines, hardware-accelerated hash and compare operations, and distributed deduplication coordination (Son et al., 2 Jan 2025, Ahmed et al., 2023).

FED will continue to evolve towards fully online, resource-adaptive, privacy-preserving, and semantically robust pipelines, driven by challenges in large-scale data curation, cloud storage, and learning-centric data management.